Principal Devops Engineer - I

Company: Deutsche Telekom Digital Labs, Gurgaon

Deutsche Telekom Digital Labs is the engineering hub of Deutsche Telekom, Europe’s #1 telecom brand. We build the digital products that millions of customers across Europe use every day. AI-first by design, we don't just adopt new technology, we build with it.

• 1 most valuable telecom brand in the world, and #11 across all brands (Brand Finance Global 500, 2026)
• Top 100 in the Fortune Global 500 (#72 in 2025)
• EUR 119 billion Group revenue in 2025
• 273 million+ mobile customers in 50+ countries

YOU ASK WHAT’S IN IT FOR YOU? WE SAY,

•AI tools, on us — Every engineer ships with AI. Coding agents, token budgets and hackfests to build what's next.

•Grow faster — Free Coursera & Udemy access, certifications and internal mobility across teams.

•Time to recharge — Uncapped leaves, birthday leave, flexible hours, and sabbatical options.

•Family first — 9 months maternity on full pay, 15 days paternity, crèche support, infertility treatment and adoption leave.

•Health & wellness — Medical cover up to ₹15L for you and your family, unlimited free doctor consults, 24/7 counselling and free Cult.fit membership.

•Money & everyday — Flexi benefits for car, fuel and books. Free meals and snacks on campus. Monthly phone and broadband allowance, plus relocation support when you need it.

Why this role exists:

We are seeking a Principal DevOps Engineer to lead Cloud and Platform engineering for AI-first teams, operating at the intersection of large-scale containerized production systems and next-generation Agentic AI and LLM deployments.

This role is responsible for building and operating highly Reliable, Secure, and Scalable platforms that support mature microservices-based workloads while enabling rapid experimentation and production rollout of Agentic AI systems. You will work closely with AI/ML, platform, and product teams across India and Europe to operationalize AI solutions at scale.

Delivery ownership:

• Define and own the cloud and platform architecture for large-scale containerized microservices and Agentic AI / LLM workloads, ensuring scalability, reliability.

• Lead CI/CD platform engineering, enabling automated build, test, security scanning, and deployment for backend services, React-based web applications, and mobile app backends.
• Enable production-grade AI platforms, supporting agent frameworks, vector databases, prompt pipelines, and inference
• Define Infrastructure as code standards, cloud account structures, networking, and environment provisioning across AWS and secondary clouds.
• Implement and enforce SRE practices: define SLIs/SLOs, error budgets, capacity and reliability targets, and lead incident response and post-incident reviews.
• Ensure end-to-end observability across services and AI workloads, including logs, metrics, traces, model performance, and cost visibility
• Embed security, compliance, and governance by design, including IAM, secrets management, network security, vulnerability management, and AI-specific controls.
• Make informed build vs. buy decisions, evaluate emerging cloud and AI infrastructure technologies, and drive continuous platform modernization.

Must-have skills and experience:

1. 10+ years of experience in DevOps / Cloud / Platform Engineering, including people management and technical leadership
2. Deep hands-on expertise with AWS, with working exposure to GCP and Azure in multi-cloud or hybrid environments
3. Proven experience operating large-scale, production-grade containerized workloads, with strong understanding of high availability, fault tolerance, and capacity planning in global teams
4. Practical experience supporting AI/ML or LLM workloads in production environments
5. Strong expertise in Kubernetes and Docker, including cluster operations, workload isolation, ingress, service meshes, and deployment strategies
6. Advanced experience with ‘Infrastructure as Code’ for cloud provisioning, networking, security controls, and environment standardization across multiple stages
7. Solid understanding of observability and reliability engineering, including metrics, logging, tracing, alerting, and defining SLIs/SLOs for distributed systems and AI services
8. Hands-on exposure with cloud security and compliance practices, including IAM design, secrets management, vulnerability scanning, and secure deployment patterns—especially for AI platforms
9. Knowledge of cloud cost optimization (FinOps), especially for AI workloads
10. Background in strong product-based organizations solving real customer-facing problems